This thesis is focused on the construction and uses of genome-scale metabolic models to efficiently obtain biofuels, such as ethanol and hydrogen. As a target organism, cyanobacterium Synechocystis sp. PCC6803 was chosen. This organism has been studied as a potential photon-fuelled production platform, for its ability to grow only from carbon dioxide, water and photons. This dissertation verses about methods to model, analyse, estimate and predict the metabolic behaviour of cells. Principal goal is to extract knowledge from the different biological aspects of an organism in order to use it for an industrial relevant objective.
This dissertation has been structured in chapters accordingly organized as the successive tasks that end up building an in silico cell that behaves as the carbon-based one. This process usually starts with the genome annotation files and ends up with a genome-scale metabolic model able to integrate ¿omics data. First objective of present thesis is to reconstruct a model of this cyanobacteria¿s metabolism that accounts for all the reactions present in it. This reconstruction had to be flexible enough as to allow growth under the different environmental conditions under which this organism grows in nature as well as to allow the integration of different levels of biological information. Once this requisite was met, environmental variations could be simulated and their effect studied under a system-wide perspective. Up to five different growth conditions were simulated on this metabolic model and differences were evaluated.
Following assignment was to define production strategies to weigh this organism¿s viability as a production platform. Genetic perturbations were simulated to design strains with an enhanced production of three industrially-relevant metabolites: succinate, ethanol and hydrogen. Resulting sets of genetic modifications for the overproduction of those metabolites are, thus, proposed. Moreover, functional reactions couplings were studied and weighted to their metabolite production importance. Finally, genome-scale metabolic models allow establishing integrative approaches to include different types of data that help to find regulatory hotspots that can be targets of genetic modification. Such regulatory hubs were identified upon light/dark shifts and general metabolism operational principles inferred. All along this process, blind spots in Synechocystis sp. PCC6803 metabolism, and more importantly, blind spots in our understanding of it, are revealed.
Overall, the work presented in this thesis unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean production platform.

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